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Earnings Forecasts Made Easy

John Price, PhD

This article describes research on a new test that is included in the Conscious Investor Investment System. The result is that we can provide earnings forecasts on selected stocks that are five times more accurate than the average of analysts' forecasts.

In the long run, the value of a stock is the discounted value of the cash that can be taken out of the company during its remaining life. The usual analysis requires two inputs. Firstly, a forecast of how much cash the company is going to generate year by year. This is generally written as a percentage growth rate starting with the current cash. Secondly, a rate at which this cash should be discounted.

There are two extra ingredients that are often pushed to the background. The first is what do we mean by the cash that can be taken out of the company. The simple answer is the earnings. A better answer is one of the variations of free cash flow. I’ll talk about this in a later article. To keep things straightforward, here I will just use earnings.

The second extra ingredient is confidence. How confident are we with the earnings forecasts? I have seen investors' eyes glaze over as they started imagining the yacht they were going to buy. They had found a stock and forecast its earnings to grow at 30% per annum. The market had missed it and now our starry-eyed investors were ready to make a killing. Alas, even though this was the forecast, what are the odds of it being accurate?

First, lets consider how well the professional do. In 1997 Lawrence Brown published a study in the Financial Analysts Journal looking at 130,000 forecasts by analysts from 1985 to 1996. He found that the average absolute error was a whopping 91.6 percent.

The difficulty is that analysts are starting behind the eight ball. They are assigned a group of companies and told to forecast their earnings. Unfortunately the earnings of companies such as Chiquita Brands and the Venator Group have more ups and downs and twists and turns than The Beast roller coaster in Cincinnati.

But we do not have this problem. No one tells us which companies to analyze. We have 10,000 companies to choose from so why not turn the problem around and focus on those companies about which we can have confidence?

One way to do this is to eyeball the historical earnings of companies and pick out those companies with earnings that grew smoothly. Next, extrapolate the data for these companies to forecast future earnings. This works to some extent, but I wanted to automate the selection/forecasting process.

This is where STAEGR™ enters, a new test I developed for my investment software Conscious Investor. It measures the stability of earnings growth from year to year and expresses it as a percentage. The maximum figure of 100% represents earnings that go up, or down, by the same percentage each year. The calculations are based on fitting an exponential curve to the historical data with more emphasis on the stability of the growth of recent earnings. Special adjustments are made for negative earnings, for extreme outliers, and for earnings near zero.

Using Value Line data, I considered all the companies with eleven years of earnings data from 1988 to 1998 inclusive. Next I divided the companies into ten groups ranging from those with the highest STAEGR over the ten years from 1988 to 1997 to those with the lowest STAEGR over this period. Each group contained 115 companies.

The next step was to calculate the earnings growth over the ten-year period using another Conscious Investor function. Earnings in 1998 were forecast using 1997 data and this historical growth figure. Finally the forecasted earnings were compared with the actual earnings in 1998.

The result of most interest to us was that the forecasted earnings of the high STAEGR group were extremely accurate. For the technically minded, the forecasted earnings explained 98 percent of the variation of the actual earnings. This can be seen in the accompanying chart.

The points represent the earnings figures, forecast and actual, of the companies in the group. Most of the points lie on or near a straight line which means that actual earnings were very close to the forecasts using historical earnings. The STAEGR of this group of 115 companies was 93% and up.

Another way of describing the results is that the average absolute error for this group of was 16 percent compared to the analyst error of 91.6 percent for all stocks. The difference is even more significant than it appears since the forecasts for the STAEGR method were for a full year whereas those of the analysts were only for the next quarter.

The results for the second group of stocks was similar. Their STAEGR ranged from 90% to 93%. This means that when we focus on stocks with the highest levels of STAEGR, say 90% and up, then the past growth or earnings is a statistically reliable predictor of earnings for the following year.

The accompanying chart shows the data for group of stocks with the lowest STAEGR.

In contrast, to the previous chart, clearly the dispersion of the data points is much higher. Notice that for some of the points the forecast is negative while the actual earnings are positive and for other points the opposite holds, positive forecasts resulting in negative outcomes.

The way these points are scattered is typical of the groups of stocks with lower STAEGR. In each case, the accuracy of the predictions using historical data was lower in two ways. Firstly the data points were more dispersed. Secondly the line of best fit had slopes further away from 1 and in many cases it was negative.

The study shows that you can have more confidence that earnings will continue to grow as in the past for stocks with a high level of STAEGR.

Of course, just because we have confidence in our forecasts of future earnings of a company does not mean that we should rush out and buy it. But it does provide a solid basis for any buy/sell/hold analysis. Sorting stocks with the high levels of STAEGR for earnings as well as sales is another of the unique features of Conscious investor.


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